1. Field of the Invention
This invention is in the field of gathering and interpreting data on the visual field of a patient, and making clinical diagnoses.
2. Background Information
Visual field interpretation, as it is commonly practiced today, is severely hampered by the fact that two practitioners will frequently come to different conclusions when interpreting a given visual field. One will call the visual field "normal," while another will classify the visual field as "abnormal."
The problem is that most eye-care practitioners throughout the world are generalists by training, rather than glaucoma specialists, neurologists, or neuro-ophthalmologists. Not having had the additional years of rigorous training associated with glaucoma, neurology or neuro-ophthalmology subspecialization, the average generalist therefore lacks the expertise to consistently interpret subtle visual fields with requisite accuracy. Grossly abnormal visual fields are readily diagnosed, but subtly abnormal, or equivocal, visual fields are often misdiagnosed, or called "normal." This diagnostic crudeness is an obvious disservice to patients.
For example, many patients with insidious forms of glaucoma, such as low-tension glaucoma, are not treated because of a generalist eye-care practicioner's inability to diagnose subtle, but yet abnormal, visual field deficits. Because patients with low-tension glaucoma have a normal, or even low, intraocular pressure, the patient is told with assurance that no disease is present, and that he or she is "fine." Nothing could be further from the truth: This patient is subject to the ravages of untreated glaucoma, which causes permanent and irreversible visual loss and much disability.
Although other tests for glaucoma have positive value, the instruments required to perform these tests, such as a laser scanner, are prohibitively expensive for most practitioners and small offices. Visual field testing, therefore, remains the "gold standard" for diagnosing diseases of the optic nerve and the visual system.
How can the accuracy of visual field testing be improved? Of interest is the autointerpretation of visual fields, especially with automated data classification systems, such as neural networks, for processing visual fields.
Neural nets have great advantages, in that they are unbiased and, as the neural net is "intelligent" and "learns" as the data base enlarges, neural nets have great flexibility.